Yes / Model checking has been extensively used to verify various systems. However, this usually has been done by experts who have a good understanding of model checking and who are familiar with the syntax of both modelling and property specification languages. Unfortunately, this is not an easy task for nonexperts to learn description languages for modelling and formal logics/languages for property specification. In particular, property specification is very daunting and error-prone for nonexperts. In this paper, we present a methodology to facilitate probabilistic model checking for nonexperts. The methodology helps nonexpert users model their systems and express their requirements without any knowledge of the modelling and property specification languages.

Contracts are legally binding and enforceable agreements among two or more parties that govern social interactions. They have been used for millennia, including in commercial transactions, employment relationships and intellectual property generation. Each contract determines obligations and powers of contracting parties. The execution of a contract needs to be continuously monitored to ensure compliance with its terms and conditions. Smart contracts are software systems that monitor and control the execution of contracts to ensure compliance. But for such software systems to become possible, contracts need to be specified precisely to eliminate ambiguities, contradictions, and missing clauses. This thesis proposes a formal specification language for contracts named Symboleo. The ontology of Symboleo is founded on the legal concepts of obligation (a kind of duty) and power (a kind of right) complemented with the concepts of event and situation that are suitable for conceptualizing monitoring tasks. The formal semantics of legal concepts is defined in terms of state machines that describe the lifetimes of contracts, obligations, and powers, as well as axioms that describe precisely state transitions. The language supports execution-time operations that enable subcontracting assignment of rights and substitution of performance to a third party during the execution of a contract. Symboleo has been applied to the formalization of contracts from three different domains as a preliminary evaluation of its expressiveness. Formal specifications can be algorithmically analyzed to ensure that they satisfy desired properties. Towards this end, the thesis presents two implemented analysis tools. One is a conformance checking tool (SymboleoPC) that ensures that a specification is consistent with the expectations of contracting parties. Expectations are defined for this tool in terms of scenarios (sequences of events) and the expected final outcome (i.e., successful/unsuccessful execution). The other tool (SymboleoPC), which builds on top of an existing model checker (nuXmv), can prove/disprove desired properties of a contract, expressed in temporal logic. These tools have been used for assessing different business contracts. SymboleoPC is also assessed in terms of performance and scalability, with positive results. Symboleo, together with its associated tools, is envisioned as an enabler for the formal verification of contracts to address requirements-level issues, at design time.

This study investigates and quantifies the effect of different specifications of the spatial weights matrix (��) on estimates and inferences in the context of a regression model using the lattice perspective with polygon-type data. The study also investigates an alternative to the specification of �� by estimating a spatial variance-covariance matrix based on known features of the spatial data. Previous literature has addressed the a priori construction of �� and selection criteria but assumes point-type data. This study’s primary contribution is the setup of a true and known benchmark that allows the comparison of the different specifications of ��. This is accomplished by using a disaggregate point-type data generating process which is then aggregated into polygon-type data. Monte Carlo simulations show that current specifications of �� used in maximum likelihood estimation for the spatial error model perform poorly. Additionally, the estimated spatial variance-covariance matrix outperforms the traditional specifications of ��.

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